K-nearest neighbor and micro-Doppler feature-based human body action identification method

A technology of human action recognition and Doppler features, applied in the field of radar technology and pattern recognition, to achieve ideal classification accuracy, good recognition effect, and wide adaptability

Inactive Publication Date: 2018-08-10
TIANJIN UNIV
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AI Technical Summary

Problems solved by technology

Traditional human motion detection uses contact sensors and video monitors. It not only needs to analyze the object to cooperate with the experiment,

Method used

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  • K-nearest neighbor and micro-Doppler feature-based human body action identification method
  • K-nearest neighbor and micro-Doppler feature-based human body action identification method
  • K-nearest neighbor and micro-Doppler feature-based human body action identification method

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Embodiment Construction

[0020] In order to describe the technical solution of the present invention more clearly, the specific implementation process of the present invention is further described as follows. The present invention is concretely realized according to the following steps:

[0021] 1. Construct radar time-frequency image database

[0022] (1) Radar simulation based on motion capture data and 3D human body model

[0023] A free human motion capture database developed by Carnegie Mellon University (CMU). During the data acquisition process, 41 points of the human body were marked and recorded by 12 120Hz infrared cameras. The library contains 2605 different types of action records. Based on the MOCAP data set, this experiment selects seven types of movements: walking, running, standing, jumping, boxing, crawling, and crawling as measurement data and establishes a 3D model of the human body for simulation.

[0024] Human body motion can be regarded as a non-rigid body motion. The human b...

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Abstract

The invention relates to a k-nearest neighbor and micro-Doppler feature-based human body action identification method. The method comprises the following steps of 1) establishing a radar time-frequency image database; 2) extracting micro-Doppler features: a) a Doppler frequency of a trunk; b) a total Doppler signal bandwidth; c) a total Doppler offset; d) a Doppler signal bandwidth of the trunk; and e) a body movement cycle; and 3) performing human body action identification by utilizing radar time-frequency images.

Description

technical field [0001] The invention belongs to the field of radar technology and pattern recognition, and relates to a human body action recognition method based on KNN and micro-Doppler features. Background technique [0002] Human action recognition based on micro-Doppler features using radar is a relatively new research field developed in recent years. It has broad application prospects and important Significance. [0003] Human body action is the most external dynamic expression of human beings, which contains powerful information. Through the recognition of actions, we can effectively understand the dynamic process of the human body, understand the information conveyed by the human body, and even analyze the behavioral intention of human body actions based on this. Human action recognition involves many disciplines such as machine vision, artificial intelligence, pattern recognition, machine learning, data mining and cognitive psychology. Traditional human motion det...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G01S13/89
CPCG01S13/89G06V40/10G06F18/24147
Inventor 侯春萍蒋天丽杨阳郎玥
Owner TIANJIN UNIV
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